Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention

Kim, Byung–Hoon; Ye, Jong Chul; Kim, Jae‐Jin

doi:10.48550/arxiv.2105.13495

Cited by 2 publications

(1 citation statement)

References 43 publications

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“…All these alternatives should also be considered as valuable candidates for dimensionality reduction of tvFC data. Of particular interest for resting-state applications is the case of autoencoders because evaluation of low dimensionality representations in this case do not necessarily require labeled data, and prior work has shown their ability to capture generative factors underlying resting-state activity ( Kim B.-H. et al, 2021 ; Kim J.-H. et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Manifold learning for fMRI time-varying functional connectivity

Gonzalez‐Castillo

Fernandez

Lam

et al. 2023

Front. Hum. Neurosci.

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Whole-brain functional connectivity (FC) measured with functional MRI (fMRI) evolves over time in meaningful ways at temporal scales going from years (e.g., development) to seconds [e.g., within-scan time-varying FC (tvFC)]. Yet, our ability to explore tvFC is severely constrained by its large dimensionality (several thousands). To overcome this difficulty, researchers often seek to generate low dimensional representations (e.g., 2D and 3D scatter plots) hoping those will retain important aspects of the data (e.g., relationships to behavior and disease progression). Limited prior empirical work suggests that manifold learning techniques (MLTs)—namely those seeking to infer a low dimensional non-linear surface (i.e., the manifold) where most of the data lies—are good candidates for accomplishing this task. Here we explore this possibility in detail. First, we discuss why one should expect tvFC data to lie on a low dimensional manifold. Second, we estimate what is the intrinsic dimension (ID; i.e., minimum number of latent dimensions) of tvFC data manifolds. Third, we describe the inner workings of three state-of-the-art MLTs: Laplacian Eigenmaps (LEs), T-distributed Stochastic Neighbor Embedding (T-SNE), and Uniform Manifold Approximation and Projection (UMAP). For each method, we empirically evaluate its ability to generate neuro-biologically meaningful representations of tvFC data, as well as their robustness against hyper-parameter selection. Our results show that tvFC data has an ID that ranges between 4 and 26, and that ID varies significantly between rest and task states. We also show how all three methods can effectively capture subject identity and task being performed: UMAP and T-SNE can capture these two levels of detail concurrently, but LE could only capture one at a time. We observed substantial variability in embedding quality across MLTs, and within-MLT as a function of hyper-parameter selection. To help alleviate this issue, we provide heuristics that can inform future studies. Finally, we also demonstrate the importance of feature normalization when combining data across subjects and the role that temporal autocorrelation plays in the application of MLTs to tvFC data. Overall, we conclude that while MLTs can be useful to generate summary views of labeled tvFC data, their application to unlabeled data such as resting-state remains challenging.

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Section: Discussionmentioning

confidence: 99%

Manifold learning for fMRI time-varying functional connectivity

Gonzalez‐Castillo

Fernandez

Lam

et al. 2023

Front. Hum. Neurosci.

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Graph-Guided Network for Irregularly Sampled Multivariate Time Series

Zhang¹,

Zeman²,

Tsiligkaridis³

et al. 2021

Preprint

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In many domains, including healthcare, biology, and climate science, time series are irregularly sampled with variable time between successive observations and different subsets of variables (sensors) are observed at different time points, even after alignment to start events. These data create multiple challenges for prevailing models that assume fully observed and fixed-length feature representations. To address these challenges, it is essential to understand the relationships between sensors and how they evolve over time. Here, we introduce RAINDROP, a graphguided network for learning representations of irregularly sampled multivariate time series. RAINDROP represents every sample as a graph, where nodes indicate sensors and edges represent dependencies between them. RAINDROP models dependencies between sensors using neural message passing and temporal selfattention. It considers both inter-sensor relationships shared across samples and those unique to each sample that can vary with time, and it adaptively estimates misaligned observations based on nearby observations. We use RAINDROP to classify time series and interpret temporal dynamics of three healthcare and human activity datasets. RAINDROP outperforms state-of-the-art methods by up to 11.4% (absolute points in F1 score), including methods that deal with irregular sampling using fixed discretization and set functions, and even in challenging leave-sensorout settings and setups that require generalizing to new patient groups.

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Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention

Cited by 2 publications

References 43 publications

Manifold learning for fMRI time-varying functional connectivity

Manifold learning for fMRI time-varying functional connectivity

Graph-Guided Network for Irregularly Sampled Multivariate Time Series

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